Low Carbon Operation Mode of Large High Power Consumption Venues Based on Big Data Mining Technology

• Published in: Journal of physics. Conference series Vol. 2418; no. 1; pp. 12092 - 12097
• Main Authors: Tang, Dehai, Zhao, Yueying, Zhang, Wenbin, Xue, Wenbin, Dou, Haoping
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2023
• Subjects: Big Data; Carbon content; Data mining; Indicators; Linear programming; Physics; Power consumption
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2418/1/012092

Abstract Given the high power consumption rate of large venues with high power consumption, excessive carbon emissions, and severe environmental pollution, this paper proposes a low-carbon operation mode for large venues with high power consumption based on big data mining technology. The DEA model of high-power consumption venues is constructed by linear programming. Then the preliminary indicators of low-carbon operation mode are selected based on big data mining technology. Finally, the selected indicators are used to set up the operation system. Through experimental analysis, it is found that the low-carbon operation mode of large venues with high power consumption can reduce 2 million kg of carbon emissions on the basis of the initial value of carbon emissions. This mode can effectively reduce the carbon emissions of high-power consumption venues and play a great role in curbing environmental pollution and improving the social benefits of venues.